RST Discourse Parsing with Coreference Information

This repository contains the code for our experiments on improving RST discourse parsing with various approaches for integrating information from a coreference resolver.

Running experiments

Coreference resolver

First, set up the coreference resolver as described here. In its main folder, run

python -m pip install -e .

to install it as library.

Preparing data

Place the contents of training and testing portions of RST-DT inside the data/data_dir folder. It should look like this:

data/data_dir/train_dir/*
data/data_dir/test_dir/*
src/

Preprocessing

This project relies on Stanford CoreNLP toolkit to preprocess the data. You can download from here and put the file run_corenlp.sh into the CoreNLP folder. Then use the following command to preprocess both the data in train_dir and in test_dir:

python preprocess.py --data_dir DATA_DIR --corenlp_dir CORENLP_DIR

Next, run the following to generate the action/relation maps, coreference clusters and train/dev split:

python main.py --prepare --train_dir TRAIN_DIR --pretrained_coref_path PATH

where --pretrained_coref_path specifies the path to pretrained coreference model, which can be downloaded from here

Training

You need to specify model type:

• 0 for the baseline model (no coreference)
• 1 for the model utilizing coreference features
• 2 for multitask model with coreference features
• 3 for multitask model without coreference features

python main.py --train --model_name YOUR_MODEL_NAME --model_type NUM --pretrained_coref_path PATH

The models are saved at data/model directory, and the training will be resumed from the last epoch if it was interrupted.

Testing

Similar to above:

python main.py --eval --eval_dir ../data/data_dir/test_dir/ --model_name YOUR_MODEL_NAME --model_type NUM --pretrained_coref_path PATH

You can add the flag --use_parseval to use standard Parseval metric instead of RST-Parseval.